class_docstr: PyTorch Lightning trainer for cnntransformer.
  
  Args:
      feature_extractor (torch.nn.Sequential, optional): the feature extractor network.
      optimizer (dict): optimizer parameters.
      lr_milestones (list): list of epoch indices. Must be increasing.
      lr_gamma (float): multiplicative factor of learning rate decay.
code: def __init__(
      self,
      feature_extractor,
      task_classifier,
      lr_milestones,
      lr_gamma,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.feat = feature_extractor
      self.classifier = task_classifier
      self.lr_milestones = lr_milestones
      self.lr_gamma = lr_gamma
  
imports: import pytorch_lightning
  import torch
  import torch.nn
  from sklearn.metrics import accuracy_score
  from kale.predict import losses
label_desc: Use image node_id 1 to create a new CNNTransformerTrainer object from inherited base classes: BaseNNTrainer with example: obj = CNNTransformerTrainer(feature_extractor, task_classifier, lr_milestones, lr_gamma)